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GH-51696: Remove stale float16 guard in from_dataframe #53727

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GH-51696: Remove stale float16 guard in from_dataframe #53727

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24 changes: 9 additions & 15 deletions python/pyarrow/interchange/from_dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -546,21 +546,15 @@ def validity_buffer_nan_sentinel(
"required which is forbidden by allow_copy=False"
)

if kind == DtypeKind.FLOAT and bit_width == 16:
# 'pyarrow.compute.is_nan' kernel not yet implemented
# for float16
raise NotImplementedError(
f"{data_type} with {null_kind} is not yet supported.")
else:
pyarrow_data = pa.Array.from_buffers(
data_dtype,
length,
[None, data_pa_buffer],
offset=offset,
)
mask = pc.is_nan(pyarrow_data)
mask = pc.invert(mask)
return mask.buffers()[1]
pyarrow_data = pa.Array.from_buffers(
data_dtype,
length,
[None, data_pa_buffer],
offset=offset,
)
mask = pc.is_nan(pyarrow_data)
mask = pc.invert(mask)
return mask.buffers()[1]

# Check for sentinel values
elif null_kind == ColumnNullType.USE_SENTINEL:
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9 changes: 4 additions & 5 deletions python/pyarrow/tests/interchange/test_conversion.py
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Expand Up @@ -319,14 +319,13 @@ def test_pandas_to_pyarrow_with_missing(np_float_str):

@pytest.mark.pandas
def test_pandas_to_pyarrow_float16_with_missing():

@rok rok Oct 14, 2026

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Perhaps it's better to just removetest_pandas_to_pyarrow_float16_with_missing test and make sure pc.is_nan on float16 is tested where all other types are tested.

# np.float16 errors if ps.is_nan is used
# pyarrow.lib.ArrowNotImplementedError: Function 'is_nan' has no kernel
# matching input types (halffloat)
# pc.is_nan has supported float16 kernel since PR #46866 (GH-45083)
np_array = np.array([0, np.nan, 2], dtype=np.float16)
df = pd.DataFrame({"a": np_array})
expected = pa.table({"a": pa.array(np_array, from_pandas=True)})

with pytest.raises(NotImplementedError):
pi.from_dataframe(df)
result = pi.from_dataframe(df)
assert result.equals(expected)


@pytest.mark.numpy
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